Table of contents¶
- 1. Information about the data given
- 2. Importing the packages needed
- 3. Load and prepare the data
- 4. Initial Data Insepction
- 5. Data Cleaning
- 6. Skills/Position Analysis
- 7. Awards Section
- 8. Dream Team
- 9. Wage Analysis
- 10. Age Analysis
- 11. Next Steps
Introduction¶
- This notebook is built to analyze in detail on what all could be done using the data provided about all players.
- This notebook would have an in-depth analysis of the some of the main data features
- Interesting Ideas:
- come up with dream team (people who are best in all positions)
- Top 3 clubs that are good in 'Attacking' and it's top 3 contributers
- Top 3 clubs that are good in 'Defense' and it's top 3 contributers
- Best club overall and it's top 3 contributers
- Top 3 nations that has best footballers
- Next best player according to wages
- How wage and players are related
- Players who aren't performing after age
- Come up with Id card that would show person's Skills, team, name, weiht, height, income
- group subset skills under common skills
- Which position would a person wanna get trained if he wants to make it quickly?
Information about the data given¶
- Columns
- row number
- IDunique id for every player
- Namename
- Ageage
- Photourl to the player's photo
- Nationalitynationality
- Flagurl to players's country flag
- Overalloverall rating
- Potentialpotential rating
- Clubcurrent club
- Club Logourl to club logo
- Valuecurrent market value
- Wagecurrent wage
- Special
- Preferred Footleft/right
- International Reputationrating on scale of 5
- Weak Footrating on scale of 5
- Skill Movesrating on scale of 5
- Work Rateattack work rate/defence work rate
- Body Typebody type of player
- Real Face
- Positionposition on the pitch
- Jersey Numberjersey number
- Joinedjoined date
- Loaned Fromclub name if applicable
- Contract Valid Untilcontract end date
- Heightheight of the player
- Weightweight of the player
- LS rating on scale of 100
- ST rating on scale of 100
- RS rating on scale of 100
- LW rating on scale of 100
- LF rating on scale of 100
- CF rating on scale of 100
- RF rating on scale of 100
- RW rating on scale of 100
- LAM rating on scale of 100
- CAM rating on scale of 100
- RAM rating on scale of 100
- LM rating on scale of 100
- LCM rating on scale of 100
- CM rating on scale of 100
- RCM rating on scale of 100
- RM rating on scale of 100
- LWB rating on scale of 100
- LDM rating on scale of 100
- CDM rating on scale of 100
- RDM rating on scale of 100
- RWB rating on scale of 100
- LB rating on scale of 100
- LCB rating on scale of 100
- CB rating on scale of 100
- RCB rating on scale of 100
- RB rating on scale of 100
- Crossing rating on scale of 100
- Finishing rating on scale of 100
- HeadingAccuracy rating on scale of 100
- ShortPassing rating on scale of 100
- Volleys rating on scale of 100
- Dribbling rating on scale of 100
- Curverating on scale of 100
- FKAccuracy rating on scale of 100
- LongPassing rating on scale of 100
- BallControl rating on scale of 100
- Acceleration rating on scale of 100
- SprintSpeed rating on scale of 100
- Agility rating on scale of 100
- Reactions rating on scale of 100
- Balance rating on scale of 100
- ShotPower rating on scale of 100
- Jumping rating on scale of 100
- Stamina rating on scale of 100
- Strength rating on scale of 100
- LongShots rating on scale of 100
- Aggression rating on scale of 100
- Interceptions rating on scale of 100
- Positioning rating on scale of 100
- Vision rating on scale of 100
- Penalties rating on scale of 100
- Composure rating on scale of 100
- Marking rating on scale of 100
- StandingTackle rating on scale of 100
- SlidingTackle rating on scale of 100
- GK Diving rating on scale of 100
- GK Handling rating on scale of 100
- GK Kicking rating on scale of 100
- GK Positioning rating on scale of 100
- GK Reflexes rating on scale of 100
- Release Clauserelease clause value
Importing the packages needed ¶
In [1]:
%matplotlib inline
%load_ext autoreload
%autoreload 2
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from math import pi
Load and prepare the data ¶
In [2]:
data_path = "data.csv"
df = pd.read_csv(data_path)
Initial Data Inspection¶
In [3]:
df.columns
Out[3]:
In [4]:
df.head()
Out[4]:
In [5]:
df.describe()
Out[5]:
In [6]:
df.info()
In [7]:
df.isna().sum()
Out[7]:
Inference¶
There are a lot of null values on the data
Data Cleaning ¶
Take out Lbs from Weight ¶
In [8]:
def weight_correction(df):
try:
value = float(df[:-3])
except:
value = 0
return value
df['Weight'] = df.Weight.apply(weight_correction)
In [9]:
df.Weight = pd.to_numeric(df.Weight)
In [10]:
df.Weight = df.Weight.replace(0, np.nan)
Change value and wage to a real number¶
In [11]:
def value_to_int(df_value):
try:
value = float(df_value[1:-1])
suffix = df_value[-1:]
if suffix == 'M':
value = value * 1000000
elif suffix == 'K':
value = value * 1000
except ValueError:
value = 0
return value
df['Value'] = df['Value'].apply(value_to_int)
df['Wage'] = df['Wage'].apply(value_to_int)
df.Value = df.Value.replace(0, np.nan)
df.Wage = df.Wage.replace(0, np.nan)
Fill the Nan Values ¶
- We will try to fill everything with some logic behind it
Fixing Weight¶
In [12]:
df.Weight.isna().sum()
Out[12]:
In [13]:
df.Weight.mean()
Out[13]:
According to livestrong data,
- The normal weight range for a player 5 feet 9 inches tall is 136 to 169 pounds.
- Since the mean weight of the player is 165 pounds and it gels with the global data, we could set the mean weight to fill the null values
In [14]:
df['Weight'].fillna(df.Weight.mean(), inplace = True)
Fixing Height¶
In [15]:
df.Height.isna().sum()
Out[15]:
In [16]:
plt.figure(figsize = (20, 10))
sns.countplot(x='Height', data=df)
plt.show()
According to livestrong data,
- The height of a player varies according to the position he takes
- Their average height was 5 feet, 11 1/2 inches tall
- We also find from the data given that most people are between 5.9 to 6-1
- So we would fill the height to 5'11"
In [17]:
df['Height'].fillna("5'11", inplace = True)
Fixing Weak Foot Rating¶
In [18]:
wf_missing = df['Weak Foot'].isna()
wf_missing.sum()
Out[18]:
In [19]:
weak_foot_prob = df['Weak Foot'].value_counts(normalize=True)
weak_foot_prob
Out[19]:
- From this we could clearly find that majority of the players would have weak foot rating as 3
- We will also fill the Foot with the same probability distribution
In [20]:
df.loc[wf_missing,'Weak Foot'] = np.random.choice(weak_foot_prob.index, size=wf_missing.sum(),p=weak_foot_prob.values)
Fixing Preferred Foot¶
In [21]:
pf_missing = df['Preferred Foot'].isna()
pf_missing.sum()
Out[21]:
In [22]:
df['Preferred Foot'].value_counts()
Out[22]:
In [23]:
foot_distribution = df['Preferred Foot'].value_counts(normalize=True)
foot_distribution
Out[23]:
- From the data, it's clear that 77% of people are right footed
- So we will fill the preferred foot in the same probability distributsion
In [24]:
df.loc[pf_missing, 'Preferred Foot'] = np.random.choice(foot_distribution.index, size = pf_missing.sum(), p=foot_distribution.values)
In [25]:
df['Preferred Foot'].value_counts()
Out[25]:
Filling Position¶
In [26]:
fp_missing = df.Position.isna()
fp_missing.sum()
Out[26]:
In [27]:
position_prob = df.Position.value_counts(normalize=True)
position_prob
Out[27]:
In [28]:
plt.figure(figsize = (20, 10))
sns.countplot(x=df.Position, data=df)
plt.show()
- From the data, it's clear that many positions have different percentage
- So we will fill the position in the same probability distributsion
In [29]:
df.loc[fp_missing, 'Position'] = np.random.choice(position_prob.index, p=position_prob.values, size=fp_missing.sum())
Filling Skill Moves¶
In [30]:
fs_missing = df['Skill Moves'].isna()
fs_missing.sum()
Out[30]:
In [31]:
skill_moves_prob = df['Skill Moves'].value_counts(normalize=True)
skill_moves_prob
Out[31]:
- We could fill the nan valeus with the same probability distribution
In [32]:
df.loc[fs_missing, 'Skill Moves'] = np.random.choice(skill_moves_prob.index, p=skill_moves_prob.values, size=fs_missing.sum())
Filling Body Type¶
In [33]:
bt_missing = df['Body Type'].isna()
bt_missing.sum()
Out[33]:
In [34]:
bt_prob = df['Body Type'].value_counts(normalize=True)
bt_prob
Out[34]:
- Not sure what how 'Neymar', 'Messi', 'Shaqiri', 'Akinfenwa', 'Courtois' are listed as body types because they are they names of the football players
- We fill the body types with the same probability distribution of the 'Normal' and 'Lean'
In [35]:
df.loc[bt_missing, 'Body Type'] = np.random.choice(['Normal', 'Lean'], p=[.63,.37], size=bt_missing.sum())
Filling Wages¶
In [36]:
wage_missing = df.Wage.isna()
wage_missing.sum()
Out[36]:
In [37]:
wage_prob = df.Wage.value_counts(normalize=True)
wage_prob
Out[37]:
- Since all good and recognized players have good wage, the wage wouldn't be filled only for players who aren't famous
- The wage distribution says that most of the players have very less wages
- We should not be filling it with mean because the mean would be really high
- So the players who are not so famous might get higher wages when compared to others who have the same talent
- So we would fill the Nan wage columns with the probability distribution of the data
In [38]:
df.loc[wage_missing, 'Wage'] = np.random.choice(wage_prob.index, p=wage_prob.values, size=wage_missing.sum())
Filling the rest of the valeus¶
- Since all features that have float64 datatypehas continuos values, we will fill it's Nan values with mean
- Randomly fill 'Contract Valid Until', 'Work Rate', 'International Reputation' , 'Jersey Number', 'Club',
In [39]:
for feature in df.columns:
if df[feature].dtype == 'float64':
df[feature].fillna(df[feature].mean(), inplace=True)
df['Contract Valid Until'].fillna(np.random.choice(df['Contract Valid Until']), inplace = True)
df['Loaned From'].fillna(np.random.choice(df['Loaned From']), inplace = True)
df['Joined'].fillna(np.random.choice(df['Joined']), inplace = True)
df['Jersey Number'].fillna(np.random.choice(df['Jersey Number']), inplace = True)
df['Club'].fillna(np.random.choice(df.Club), inplace = True)
df['Work Rate'].fillna(np.random.choice(df['Work Rate']), inplace = True)
df['International Reputation'].fillna(np.random.choice(df['International Reputation']), inplace = True)
Fill the rest of the NaN data with 0¶
In [40]:
df.fillna(0, inplace = True)
Grouping similar skills together¶
- Here, we are grouping the skills together and generalizing it to 8 categories
- These 8 categories would let us know which position that player would take
- We do this because we could analyze the players better and positon them accordingly
In [41]:
def defending(data):
return data[['Marking', 'StandingTackle',
'SlidingTackle']].mean().mean()
def general(data):
return data[['HeadingAccuracy', 'Dribbling', 'Curve',
'BallControl']].mean().mean()
def mental(data):
return data[['Aggression', 'Interceptions', 'Positioning',
'Vision','Composure']].mean().mean()
def passing(data):
return data[['Crossing', 'ShortPassing',
'LongPassing']].mean().mean()
def mobility(data):
return data[['Acceleration', 'SprintSpeed',
'Agility','Reactions']].mean().mean()
def power(data):
return data[['Balance', 'Jumping', 'Stamina',
'Strength']].mean().mean()
def rating(data):
return data[['Potential', 'Overall']].mean().mean()
def shooting(data):
return data[['Finishing', 'Volleys', 'FKAccuracy',
'ShotPower','LongShots', 'Penalties']].mean().mean()
In [42]:
# renaming a column
df.rename(columns={'Club Logo':'Club_Logo'}, inplace=True)
# adding these categories to the data
df['Defending'] = df.apply(defending, axis = 1)
df['General'] = df.apply(general, axis = 1)
df['Mental'] = df.apply(mental, axis = 1)
df['Passing'] = df.apply(passing, axis = 1)
df['Mobility'] = df.apply(mobility, axis = 1)
df['Power'] = df.apply(power, axis = 1)
df['Rating'] = df.apply(rating, axis = 1)
df['Shooting'] = df.apply(shooting, axis = 1)
In [43]:
players = df[['Name','Defending','General','Mental','Passing',
'Mobility','Power','Rating','Shooting','Flag','Age',
'Nationality', 'Photo', 'Club_Logo', 'Club']]
Skills/Position Analysis ¶
Number of footballers available in each position¶
In [44]:
plt.figure(figsize = (20, 10))
ax = sns.countplot(x='Position', data=df, order = df['Position'].value_counts().index)
ax.set_title(label = 'Number of footballers available in each position', fontsize = 20)
plt.show()
Analyzing top 5 features on all skills ¶
In [45]:
player_features = (
'Acceleration', 'Aggression', 'Agility',
'Balance', 'BallControl', 'Composure',
'Crossing', 'Dribbling', 'FKAccuracy',
'Finishing', 'GKDiving', 'GKHandling',
'GKKicking', 'GKPositioning', 'GKReflexes',
'HeadingAccuracy', 'Interceptions', 'Jumping',
'LongPassing', 'LongShots', 'Marking', 'Penalties'
)
from math import pi
idx = 1
plt.figure(figsize=(15,45))
for position_name, features in df.groupby(df['Position'])[player_features].mean().iterrows():
top_features = dict(features.nlargest(5))
# number of variable
categories=top_features.keys()
N = len(categories)
# We are going to plot the first line of the data frame.
# But we need to repeat the first value to close the circular graph:
values = list(top_features.values())
values += values[:1]
# What will be the angle of each axis in the plot? (we divide the plot / number of variable)
angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]
# Initialise the spider plot
ax = plt.subplot(10, 3, idx, polar=True)
# Draw one axe per variable + add labels labels yet
plt.xticks(angles[:-1], categories, color='grey', size=8)
# Draw ylabels
ax.set_rlabel_position(0)
plt.yticks([25,50,75], ["25","50","75"], color="grey", size=7)
plt.ylim(0,100)
plt.subplots_adjust(hspace = 0.5)
# Plot data
ax.plot(angles, values, linewidth=1, linestyle='solid')
# Fill area
ax.fill(angles, values, 'b', alpha=0.1)
plt.title(position_name, size=11, y=1.1)
idx += 1
Inference¶
- From the position graph and the skill graph above, we get to know the most important skills that are required for each position
- This helps:
- the management to look out for the right set of skills from each players and to buy players with the skills they are looking for
- the to-be professionals to understand which skill they need to develop in order to get the position they need
More stats on skills and special moves ¶
In [46]:
sns.set(style = 'dark', palette = 'colorblind', color_codes = True)
x = df.Special
plt.figure(figsize = (12, 8))
ax = sns.distplot(x, bins = 50, kde = False, color = 'm')
ax.set_xlabel(xlabel = 'Special score range', fontsize = 16)
ax.set_ylabel(ylabel = 'Count of the Players',fontsize = 16)
ax.set_title(label = 'Histogram for the Speciality Scores of the Players', fontsize = 20)
plt.show()
In [47]:
sns.scatterplot(x = 'Special', y='Wage', data=df)
Out[47]:
Inference¶
- Skills and wage are not completely positively correlated
Top 5 nations that have the best skilled footballers¶
In [48]:
plt.rcParams['figure.figsize'] = (20, 10)
skill_df = df[df['Skill Moves'] == 5][['Name','Nationality']]
sns.countplot(x='Nationality', data=skill_df, order=skill_df.Nationality.value_counts().iloc[:5].index)
Out[48]:
Awards Section¶
- This section would have the top 5/ top 3 contibutors for many sections
Top footballer producing nations ¶
In [49]:
import squarify
df.Nationality.value_counts().nlargest(5).plot(kind='bar')
Out[49]:
Player weight distribution in top 5 footballer producing countries ¶
In [50]:
countries = df.Nationality.value_counts().nlargest(5).index
In [51]:
data_countries = df[df['Nationality'].isin(countries)]
In [52]:
plt.rcParams['figure.figsize'] = (12, 7)
ax = sns.violinplot(x = data_countries['Nationality'], y = data_countries['Weight'], palette = 'colorblind')
ax.set_xlabel(xlabel = 'Countries', fontsize = 9)
ax.set_ylabel(ylabel = 'Weight in lbs', fontsize = 9)
ax.set_title(label = 'Distribution of Weight of players from different countries', fontsize = 20)
Out[52]:
Club-level Analysis ¶
In [53]:
import matplotlib.image as mpimg
import requests
def print_club_flag(clubs):
fig = plt.figure(figsize=(10,10))
for index, club in enumerate(clubs):
logo = df[df['Club'] == club]['Club_Logo'].iloc[0]
logo_image = "img_club_logo.jpg"
logo_flag = requests.get(logo).content
with open(logo_image, 'wb') as handler:
handler.write(logo_flag)
img=mpimg.imread(logo_image)
ax = fig.add_subplot(1, 6, index+1, xticks=[], yticks=[])
fig.tight_layout()
ax.imshow(img, interpolation="lanczos")
ax.set_title("%d. %s" %(index+1, club))
def print_national_flag(nations):
fig = plt.figure(figsize=(10, 10))
for index, nation in enumerate(nations):
logo = df[df['Nationality'] == nation]['Flag'].iloc[0]
logo_image = "img_nation_logo.jpg"
logo_flag = requests.get(logo).content
with open(logo_image, 'wb') as handler:
handler.write(logo_flag)
img=mpimg.imread(logo_image)
ax = fig.add_subplot(1, 6, index+1, xticks=[], yticks=[])
fig.tight_layout()
ax.imshow(img, interpolation="lanczos")
ax.set_title("%d. %s" %(index+1, nation))
Best football clubs ¶
- Here are the top 5 football clubs w.r.t their overall rating
In [54]:
d = {'Overall': 'Average_Rating'}
best_overall_club_df = df.groupby('Club').agg({'Overall':'mean'}).rename(columns=d)
clubs = best_overall_club_df.Average_Rating.nlargest(5).index
clubs_list = []
print_club_flag(clubs)
Clubs that have the best Attack ¶
- Here are the top 5 clubs that specialize in attack
In [55]:
attck_list = ['Shooting', 'Power', 'Passing']
best_attack_df = players.groupby('Club')[attck_list].sum().sum(axis=1)
clubs = best_attack_df.nlargest(5).index
print_club_flag(clubs)
Clubs that have the best Defense ¶
- Here are the top 5 clubs that specialize in defense
- More on interpolation
In [56]:
best_defense_df = players.groupby('Club')['Defending'].sum()
clubs = best_defense_df.nlargest(5).index
print_club_flag(clubs)
Nation-level Analysis ¶
Best footballing nations ¶
In [57]:
d = {'Overall': 'Average_Rating'}
best_overall_country_df = df.groupby('Nationality').agg({'Overall':'mean'}).rename(columns=d)
nations = best_overall_country_df.Average_Rating.nlargest(5).index
print_national_flag(nations)
In [58]:
best_3_uae = df[df['Nationality'] == 'United Arab Emirates']['Overall'].nlargest(3)
print(best_3_uae)
uae_df = df[df['Nationality'] == 'United Arab Emirates']
uae_df[uae_df['Overall'].isin(best_3_uae)]['Name']
Out[58]:
Nations that has the best Attack¶
In [59]:
best_attack_nation_df = players.groupby('Nationality')[attck_list].sum().sum(axis=1)
nations = best_attack_nation_df.nlargest(5).index
print_national_flag(nations)
Nations that has the best Defense¶
In [60]:
best_defense_nation_df = players.groupby('Nationality')['Defending'].sum()
nations = best_defense_nation_df.nlargest(5).index
print_national_flag(nations)
In [61]:
import requests
import random
from math import pi
import matplotlib.image as mpimg
from matplotlib.offsetbox import (OffsetImage,AnnotationBbox)
def details(row, title, image, age, nationality, photo, logo, club):
flag_image = "img_flag.jpg"
player_image = "img_player.jpg"
logo_image = "img_club_logo.jpg"
img_flag = requests.get(image).content
with open(flag_image, 'wb') as handler:
handler.write(img_flag)
player_img = requests.get(photo).content
with open(player_image, 'wb') as handler:
handler.write(player_img)
logo_img = requests.get(logo).content
with open(logo_image, 'wb') as handler:
handler.write(logo_img)
r = lambda: random.randint(0,255)
colorRandom = '#%02X%02X%02X' % (r(),r(),r())
if colorRandom == '#ffffff':colorRandom = '#a5d6a7'
basic_color = '#37474f'
color_annotate = '#01579b'
img = mpimg.imread(flag_image)
#flg_img = mpimg.imread(logo_image)
plt.figure(figsize=(15,8))
categories=list(players)[1:]
coulumnDontUseGraph = ['Flag', 'Age', 'Nationality', 'Photo', 'Logo', 'Club']
N = len(categories) - len(coulumnDontUseGraph)
angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]
ax = plt.subplot(111, projection='polar')
ax.set_theta_offset(pi / 2)
ax.set_theta_direction(-1)
plt.xticks(angles[:-1], categories, color= 'black', size=17)
ax.set_rlabel_position(0)
plt.yticks([25,50,75,100], ["25","50","75","100"], color= basic_color, size= 10)
plt.ylim(0,100)
values = players.loc[row].drop('Name').values.flatten().tolist()
valuesDontUseGraph = [image, age, nationality, photo, logo, club]
values = [e for e in values if e not in (valuesDontUseGraph)]
values += values[:1]
ax.plot(angles, values, color= basic_color, linewidth=1, linestyle='solid')
ax.fill(angles, values, color= colorRandom, alpha=0.5)
axes_coords = [0, 0, 1, 1]
ax_image = plt.gcf().add_axes(axes_coords,zorder= -1)
ax_image.imshow(img,alpha=0.5)
ax_image.axis('off')
ax.annotate('Nationality: ' + nationality.upper(), xy=(10,10), xytext=(103, 138),
fontsize= 12,
color = 'white',
bbox={'facecolor': color_annotate, 'pad': 7})
ax.annotate('Age: ' + str(age), xy=(10,10), xytext=(43, 180),
fontsize= 15,
color = 'white',
bbox={'facecolor': color_annotate, 'pad': 7})
ax.annotate('Team: ' + club.upper(), xy=(10,10), xytext=(92, 168),
fontsize= 12,
color = 'white',
bbox={'facecolor': color_annotate, 'pad': 7})
arr_img_player = plt.imread(player_image, format='jpg')
imagebox_player = OffsetImage(arr_img_player)
imagebox_player.image.axes = ax
abPlayer = AnnotationBbox(imagebox_player, (0.5, 0.7),
xybox=(313, 223),
xycoords='data',
boxcoords="offset points"
)
arr_img_logo = plt.imread(logo_image, format='jpg')
imagebox_logo = OffsetImage(arr_img_logo)
imagebox_logo.image.axes = ax
abLogo = AnnotationBbox(imagebox_logo, (0.5, 0.7),
xybox=(-320, -226),
xycoords='data',
boxcoords="offset points"
)
ax.add_artist(abPlayer)
ax.add_artist(abLogo)
plt.title(title, size=50, color= basic_color)
In [62]:
# defining a polar graph
def get_id_card(id = 0):
if 0 <= id < len(df.ID):
details(row = players.index[id],
title = players['Name'][id],
age = players['Age'][id],
photo = players['Photo'][id],
nationality = players['Nationality'][id],
image = players['Flag'][id],
logo = players['Club_Logo'][id],
club = players['Club'][id])
else:
print('The base has 17917 players. You can put positive numbers from 0 to 17917')
Top 5 footballers ¶
- This gives a pictorial representation of the top 5 footballers
- Thanks Roshan sharma for the ID card code. Really well done!!!
In [63]:
best_footballers = df['Overall'].nlargest(5)
for index in best_footballers.index:
get_id_card(index)
Dream Team ¶
- Ever dreamt of a team which would have all your favourite players?
- This team below has the best players in all positions :)
In [64]:
df.loc[df.groupby(df['Position'])['Potential'].idxmax()][['Name', 'Position', 'Overall', 'Age', 'Nationality', 'Club']]
Out[64]:
Wage Analysis ¶
In [65]:
#### sns.set(style = 'dark', palette = 'colorblind', color_codes = True)
x = df.Wage
plt.figure(figsize = (12, 8))
ax = sns.distplot(x, bins = 50, kde = False, color = 'm')
ax.set_xlabel(xlabel = 'Player Wage', fontsize = 16)
ax.set_ylabel(ylabel = 'Player Count',fontsize = 16)
ax.set_title(label = 'Histogram that shows the wage of the Players', fontsize = 20)
plt.show()
In [66]:
df[df['Wage']>300000][['Name','Age','Wage']]
Out[66]:
Inference¶
- Looks like the wage is highly skewed
- Only a handful of people get more than 300,000 Euros
In [67]:
df.groupby('Wage')['Overall'].mean().plot()
Out[67]:
Inference¶
- It's very evident that the wage is getting higher only for star performers
Age Analysis ¶
In [68]:
df.groupby('Age')['Overall'].mean().plot()
Out[68]:
Inference¶
- The overall performance of the players dips after 30
- Let us look at why it has gone up after 43
In [69]:
sns.countplot(x='Age', data=df)
Out[69]:
In [70]:
df[df['Age']>40][['Name','Overall','Age','Nationality']]
Out[70]:
Inference¶
- There is only a handful of people are there > 40
- Mr.Perez is surely an outlier and a Mexico's pride!!!
In [87]:
new_wage = df[df['Wage']>10000]
new_wage['age_group'] = pd.cut(new_wage.Age, bins=4)
#new_wage.plot(x='age_group', y='Wage', kind = 'bar')
ax = new_wage.boxplot(column='Wage', by='age_group', showmeans=True)
ax.set_xlabel(xlabel = 'Age Group', fontsize = 20)
ax.set_ylabel(ylabel = 'Wage', fontsize = 20)
Out[87]:
Inference¶
- Players Are In High Demand In Their Mid-20s
Next Steps ¶
- This section would involve some more analysis predictions like:
- Who would be the next big star?
- What all would contribute to get a better salary?
- Please comment on which predctions/analysis would you need
In [72]:
"""positions = ['CAM', 'CB', 'CDM', 'CF', 'CM', 'LAM',
'LB', 'LCB', 'LCM', 'LDM', 'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RF',
'RM', 'RS', 'RW', 'RWB']"""
Out[72]:
In [73]:
"""for i in positions:
print('\n\n','Top 10', i, 'in FIFA 19', '\n')
temp_df = df[df.Position == i]
print(temp_df.sort_values(i, ascending=False).head(10).reset_index()[['Name', i]])
#print(df.sort_values(temp_df, ascending=False).head(10).reset_index()[['Name', 'Nationality', 'Club', 'Overall']])"""
Out[73]: